Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Query-Oriented Summarization Based on Neighborhood Graph Model
ICCPOL '09 Proceedings of the 22nd International Conference on Computer Processing of Oriental Languages. Language Technology for the Knowledge-based Economy
Learning instance specific distances using metric propagation
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Relevance feature mapping for content-based image retrieval
Proceedings of the Tenth International Workshop on Multimedia Data Mining
Mass estimation and its applications
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Relevance feature mapping for content-based multimedia information retrieval
Pattern Recognition
BoostML: an adaptive metric learning for nearest neighbor classification
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Machine Learning
Probabilistic co-relevance for query-sensitive similarity measurement in information retrieval
Information Processing and Management: an International Journal
Exploiting relevance, coverage, and novelty for query-focused multi-document summarization
Knowledge-Based Systems
Who is repinning?: predicting a brand's user interactions using social media retrieval
Proceedings of the Thirteenth International Workshop on Multimedia Data Mining
| Hi-index | 0.00 |
Similarity measure is one of the keys of a high-performance content-based image retrieval (CBIR) system. Given a pair of images, existing similarity measures usually produce a static and constant similarity score. However, an image can usually be perceived with different meanings and therefore, the similarity between the same pair of images may change when the concept being queried changes. This paper proposes a query-sensitive similarity measure, Qsim, which takes the concept being queried into account in measuring image similarities, by exploiting the query image as well as the images labeled by user in the relevance feedback process. Experimental comparisons to state-of-the-art techniques show that Qsim has superior performance.